药物HIVE:针对特定目标的空间药物设计和优化,使用层次化的生成模型
bioRxiv : the preprint server for biology
|January 8, 2024
概括
DrugHIVE是一种新的深度生成模型,通过使细粒度分子生成能够加速药物设计. 这种人工智能方法提升了新的设计,优化和脚手架跳跃,即使对于具有挑战性的蛋白质标.
科学领域:
- 计算化学和化学信息学
- 人工智能在药物发现中的作用
- 结构生物学和分子建模.
背景情况:
- 基于结构的药物设计 (SBDD) 在早期药物开发中至关重要,利用计算方法和结构数据的进步.
- 虚拟选方法具有影响力,但由于庞大的化学空间和计算限制,面临可扩展性挑战.
- 深度生成建模提供了一种强大的AI驱动方法,通过从数据中学习分子关系来克服局限性.
结论:
- 药物HIVE显著加快了常见的药物设计工作流程,提供了更好的控制和效率.
- 该模型的可扩展性和适用于预测的蛋白质结构的应用性扩大了人工智能驱动的药物发现的范围.
- 能够为大多数人体蛋白质组产生高质量的类似药物分子,包括具有未解决结构的目标.
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